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SQL Server 2008 also contains improvements to bitmap operators which help reduce data movement across threads in parallel queries. The bitmap can be used to reduce each row to a single bit. Because two bitmaps can be intersected or unioned ef ciently, this model allows SQL Server to join two tables simply by performing a bitmap operation. This allows each dimension table to be queried to identify qualifying rows, creating a bitmap that is then sent to the thread(s) scanning the fact table. These bitmaps are applied using a probe lter applied as a non-sargable predicate to the fact table. This is somewhat like a special on-the- y index, created just for data warehouse queries of this pattern. One limitation in SQL Server that still affects large tables, such as the fact table in a data warehouse con guration, is that there can only be 200 steps in a histogram. Very large tables often have more than 200 steps of interesting data distribution data, so sometimes queries may be overestimated or underestimated as a result of this limitation, even with full-scan statistics. Luckily, ltered statistics can be used to alleviate this problem somewhat it is possible to create ltered statistics for the range that de nes a partition and then have that be used to estimate cardinality. As many queries on partitioned tables are over the current partition in a date range, this covers a reasonable number of the scenarios that were not covered as well in SQL Server 2005.

Updates are an interesting area within query processing. In addition to many of the challenges faced while optimizing traditional SELECT queries, update optimization also considers physical optimizations such as how many indexes need to be touched for each row, whether to process the updates one index at a time or all at once, and how to avoid unnecessary locking deadlocks while processing changes as quickly as possible. The Optimizer contains a number of features that are speci c to updates that help make queries complete as quickly as possible. In this section, we discuss a number of these optimizations. In this section, the term update processing actually includes all top-level commands that change data. This includes INSERT, UPDATE, DELETE, and (as of SQL Server 2008) MERGE. As you see in this section, SQL Server treats these commands almost identically. Every update query in SQL Server is composed of the same basic operations:

Determines what rows are changed (inserted, updated, deleted, merged) Calculates the new values for any changed columns Applies the change to the table and any nonclustered index structures.

11. Click OK to start replacing all occurrences of textBox1 with tbMessage. You should see a dialog box that will give you a preview of all the changes that will be made by refactoring. The upper section shows you where the string was found, and the bottom portion shows you what the change will look like if applied to the selected item. The check boxes allow you to select only the changes you really want to perform. One neat feature is that they are organized by type of change: one for the code elements, one for the comments, and finally one for the strings. Look at the top of Figure 5-18 to see the Preview Changes dialog box s upper window completely scrolled, and look at the bottom of Figure 5-18 for the first selected change preview.

CREATE TABLE update1 (col1 INT PRIMARY KEY IDENTITY, col2 INT, col3 INT); INSERT INTO update1 (col2, col3) VALUES (2, 3);

The INSERT query has an operator called a Constant Scan. A Constant Scan is a special operator in the relational algebra that generates rows without reading them from a table. If you are inserting a row into a table, it doesn t really have an existing table, so this operator creates a row for the insert operator to process. The Compute Scalar operation evaluates the values to be inserted. In our example, these are constants, but they could be arbitrary scalar expressions or scalar subqueries. Finally, the insert operator physically updates the primary key-clustered index. Figure 8-50 shows how UPDATE plans are represented.

W3C (World Wide Web Consortium) The international standards group for the Web WAP (Wireless Application Protocol) The protocol used to send information, specifically text-based Web pages, to a cellular phone or wireless system WDDX (Web Distributed Data eXchange) Describes complex data structures, such as arrays and records, so that they can be moved among applications WIDL (Web Interface Definition Language) Enables application-to-application communication WIDL uses a meta-data syntax to define APIs A DOM maps application or document data elements to program variables WML (Wireless Markup Language) Uses WAP to convert HTML Web pages or XML data into a format that cell phones, digital assistants, or other wireless systems can display on the small screen Worm A malicious program that penetrates a computer system and progressively travels throughout the system damaging data along its path A worm may hide in an executable program that is transferred from computer to computer WYSIWIG Stands for what you see is what you get, which implies that the text on the computer screen will look the same when it is printed on a page The term is usually associated with word processing

UPDATE update1 SET col2 = 5;